Distinctive Linguistic Features Research Articles

Identifying Reddit Users at a High Risk of Suicide and Their Linguistic Features During the COVID-19 Pandemic: Growth-Based Trajectory Model.

Suicide has emerged as a critical public health concern during the COVID-19 pandemic. With social distancing measures in place, social media has become a significant platform for individuals expressing suicidal thoughts and behaviors. However, existing studies on suicide using social media data often overlook the diversity among users and the temporal dynamics of suicide risk. By examining the variations in post volume trajectories among users on the r/SuicideWatch subreddit during the COVID-19 pandemic, this study aims to investigate the heterogeneous patterns of change in suicide risk to help identify social media users at high risk of suicide. We also characterized their linguistic features before and during the pandemic. We collected and analyzed post data every 6 months from March 2019 to August 2022 for users on the r/SuicideWatch subreddit (N=6163). A growth-based trajectory model was then used to investigate the trajectories of post volume to identify patterns of change in suicide risk during the pandemic. Trends in linguistic features within posts were also charted and compared, and linguistic markers were identified across the trajectory groups using regression analysis. We identified 2 distinct trajectories of post volume among r/SuicideWatch subreddit users. A small proportion of users (744/6163, 12.07%) was labeled as having a high risk of suicide, showing a sharp and lasting increase in post volume during the pandemic. By contrast, most users (5419/6163, 87.93%) were categorized as being at low risk of suicide, with a consistently low and mild increase in post volume during the pandemic. In terms of the frequency of most linguistic features, both groups showed increases at the initial stage of the pandemic. Subsequently, the rising trend continued in the high-risk group before declining, while the low-risk group showed an immediate decrease. One year after the pandemic outbreak, the 2 groups exhibited differences in their use of words related to the categories of personal pronouns; affective, social, cognitive, and biological processes; drives; relativity; time orientations; and personal concerns. In particular, the high-risk group was discriminant in using words related to anger (odds ratio [OR] 3.23, P<.001), sadness (OR 3.23, P<.001), health (OR 2.56, P=.005), achievement (OR 1.67, P=.049), motion (OR 4.17, P<.001), future focus (OR 2.86, P<.001), and death (OR 4.35, P<.001) during this stage. Based on the 2 identified trajectories of post volume during the pandemic, this study divided users on the r/SuicideWatch subreddit into suicide high- and low-risk groups. Our findings indicated heterogeneous patterns of change in suicide risk in response to the pandemic. The high-risk group also demonstrated distinct linguistic features. We recommend conducting real-time surveillance of suicide risk using social media data during future public health crises to provide timely support to individuals at potentially high risk of suicide.

Tweeting environmental pollution: Analyzing twitter language to uncover its correlation with county-level obesity rates in the United States

BackgroundEnvironmental pollution has been linked to obesogenic tendencies. Using environmental-related posts from Twitter (now known as X) from U.S. counties, we aim to uncover the association between Twitter linguistic data and U.S. county-level obesity rates. MethodsAnalyzing nearly 300 thousand tweets from January 2020 to December 2020 across 207 U.S. counties, using an innovative Differential Language Analysis technique and drawing county-level obesity data from the 2020 Food Environment Atlas to identify distinct linguistic features in Twitter relating to environmental-related posts correlated with socioeconomic status (SES) index indicators, obesity rates, and obesity rates controlled for SES index indicators. We also employed predictive modeling to estimate Twitter language's predictive capacity for obesity rates. ResultsResults revealed a negative correlation between environmental-related tweets and obesity rates, both before and after adjusting for SES. Contrarily, non-environmental-related tweets showed a positive association with higher county-level obesity rates, indicating that individuals living in counties with lower obesity rates tend to tweet environmental-related language more frequently than those living in counties with higher obesity rates. The findings suggest that linguistic patterns and expressions employed in discussing environmental-related themes on Twitter can offer unique insights into the prevailing cross-sectional patterns of obesity rates. ConclusionsAlthough Twitter users are a subset of the general population, incorporating environmental-related tweets and county-level obesity rates and using a novel language analysis technique make this study unique. Our results indicated that Twitter users engaging in more active dialog about environmental concerns might exhibit healthier lifestyle practices, contributing to reduced obesity rates.

Artificial intelligence classifies primary progressive aphasia from connected speech.

Neurodegenerative dementia syndromes, such as Primary Progressive Aphasias (PPA), have traditionally been diagnosed based in part on verbal and nonverbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and also regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we first harnessed the capabilities of artificial intelligence (AI) and Natural Language Processing (NLP) to perform unsupervised classification of short, connected speech samples from 78 PPA patients. We then used NLP to identify linguistic features that best dissociate the three PPA variants. Large Language Models (LLMs) discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. In the subsequent supervised classification, seventeen distinctive features emerged, including the observation that separating verbs into high and low-frequency types significantly improves classification accuracy. Using these linguistic features derived from the analysis of short, connected speech samples, we developed a classifier that achieved 97.9% accuracy in classifying the four groups (three PPA variants and healthy controls). The data-driven section of this study showcases the ability of LLMs to find natural partitioning in the speech of patients with PPA consistent with conventional variants. In addition, the work identifies a robust set of language features indicative of each PPA variant, emphasizing the significance of dividing verbs into high and low-frequency categories. Beyond improving diagnostic accuracy, these findings enhance our understanding of the neurobiology of language processing.

DFNet: Decoupled Fusion Network for Dialectal Speech Recognition

Deep learning is often inadequate for achieving effective dialect recognition in situations where data are limited and model training is complex. Differences between Mandarin and dialects, such as the varied pronunciation variants and distinct linguistic features of dialects, often result in a significant decline in recognition performance. In addition, existing work often overlooks the similarities between Mandarin and its dialects and fails to leverage these connections to enhance recognition accuracy. To address these challenges, we propose the Decoupled Fusion Network (DFNet). This network extracts acoustic private and shared features of different languages through feature decoupling, which enhances adaptation to the uniqueness and similarity of these two speech patterns. In addition, we designed a heterogeneous information-weighted fusion module to effectively combine the decoupled Mandarin and dialect features. This strategy leverages the similarity between Mandarin and its dialects, enabling the sharing of multilingual information, and notably enhance the model’s recognition capabilities on low-resource dialect data. An evaluation of our method on the Henan and Guangdong datasets shows that the DFNet performance has improved by 2.64% and 2.68%, respectively. Additionally, a significant number of ablation comparison experiments demonstrate the effectiveness of the method.

Turning talk into text: the representation of contemporary urban vernaculars in Swedish fiction

Abstract This article examines the literary representation of contemporary urban vernaculars (CUV) in fiction. It focuses specifically on four Swedish novels published in the last ten years, whose narratives are set in the urban and increasingly multilingual, migrant-rich and class-stratified peripheral areas of Stockholm, the capital of Sweden. The analysis centers on how they are situated in these urban peripheries, using written representations of spoken, non-standard Swedish CUV as symbolic resources to give authenticity to the narratives. We examine the distinctive linguistic features that are employed to evoke the imagination of CUV, and how these are used to build the fictional characters and to create certain recognizable social personas and practices. We also discuss the linguistic features that are available but are not exploited to represent the fictional characters’ ways of speaking, and possible reasons why this is so. Finally, we examine how the novels exploit contrasts between registers, particularly between CUV and adult second-language speaker styles and between CUV and standard Swedish, and with what effects. The findings are discussed in the context of the broader social discourses about language, migration, CUV and adult second-language speakers in present-day Sweden.

A tale of two platforms: A comparative analysis of language use in consumer complaints on Reddit and Spotify Community

AbstractConsumer complaints play a crucial role for companies in understanding customer needs and spotting possible areas of improvement. In the digital era, online platforms have become popular venues for users to voice their concerns. According to Communication Accommodation Theory (CAT) and recipient design, consumers may have different recipients in mind when writing a complaint and tailor their language accordingly. This study aims to examine language variations in consumer complaints on an independent platform and a corporate platform, exploring how consumers adjust their language based on the intended recipients of their complaints. Comments under complaint posts from Reddit's dedicated subreddit, r/Spotify, and from the Spotify Community were extracted and analyzed using the Linguistic Inquiry and Word Count (LIWC), a text analysis tool. This comparative analysis identified and compared various linguistic categories within the consumer complaints from the perspectives of formality, emotion, and assertion. The results showed that complaints on the corporate platform (1) used more formal language, (2) contained more negative emotional tones and emotion words, and (3) exhibited more assertiveness compared to those on the independent platform. Understanding the distinct linguistic features in consumer complaints on different platforms can help companies tailor their responses and address customer concerns effectively. This research contributes to the growing field of online consumer complaining behavior by uncovering how language is employed within different online communities depending on the message recipient. Furthermore, it demonstrates the potential of LIWC as a valuable tool for analyzing unstructured consumer‐generated content across diverse digital platforms, offering insights into user sentiment and experiences.

A Linguistic-stylistic Analysis of Lewis Carroll’s Jabberwocky

This article attempts at conducting a stylistic analytical study of a renowned poem by Lewis Carroll, Jabberwocky. It tries to unveil the variations and schemes of linguistic stylistics that can be found in the poem. Carroll’s famed poem captivates with its linguistic deviations and imaginative storytelling. This scholarly investigation embarks on a comprehensive stylistic analysis of the iconic poem, delving into its distinctive linguistic features. By exploring Carroll’s masterful manipulation of language, especially in the realm of linguistics and stylistics, this research aims at unraveling the poetic brilliance and esthetic allure of Jabberwocky. The inquiry commences with a meticulous examination of Carroll’s artful utilization of linguistic play, boldly challenging conventional language norms and stretching the reader’s comprehension. The poem’s unique blend of real and invented words creates boundless creativity. His language exhibits distinctiveness and versatility, employing a diverse array of grammatical and poetic devices to manifest his unique style, thereby adding depth and intrigue to the poem. The methodology entails a comprehensive stylistic analysis based on Geoffrey Leech’s eight models of deviations and schemes. Overall, this article will show how linguistic-stylistic bridges the gap between language and literature studies, enriching researchers’ understanding of poetry within applied linguistics by scrutinizing the poet’s linguistic devices, structures, and effects. The article will encompass a theoretical explanation within the domains of literary analysis, poetic language analysis, and cognitive poetry in the realm of linguistic analysis.

Distinctive Linguistic Features of Egyptian-Arabic Dialect of Farafra Oasis: The Problem of Local Identity

Distinctive Linguistic Features of Egyptian-Arabic Dialect of Farafra Oasis: The Problem of Local Identity

On the issue of defining the concepts of image and PR texts in the modern media space

The subject of this article is the image and business reputation, which are largely determined by a huge corpus of various image and PR texts created daily within the framework of numerous mass media. The purpose of the work is to give definitions of the concepts of image and PR texts in the modern media space, to determine their similarities and differences through content and discourse analysis of materials of scientific periodicals. Socio-political, social and economic media texts in the Russian media space have many distinctive linguistic and non-linguistic features due to the national specifics and social development of our country. Reputation is often formed not only by image media texts, but also by a number of other types of texts represented in mass media with image potential. The scientific novelty of the study lies in the fact that with the existing problem of typologization of media texts, the hypothesis is introduced that PR texts in their broad sense are nothing more than a subgroup of image media texts present in the media field of the main spheres – journalism, advertising and PR activities. The author comes to the conclusion that the instability and dynamism of the terminological apparatus gives a certain "freedom" of action when selecting research material. The ways in which image specialists work and the genre nature of image media texts themselves have become much more complicated. Image media texts can be considered as journalistic text, advertising text, PR text.

Poster Symposium: Leveraging Digital Technology to Capture Highly Nuanced Neuropsychological Behavior: Realizing the Vision of the Boston Process Approach to Neuropsychological Assessment

Summary AbstractNeuropsychological test scores tap a number of underlying cognitive abilities. Examining the means by which omnibus scores are achieved provides considerable information regarding brain - behavior relationships and a richer context for clinical interpretation. This examination is the core tenant of the Boston Process Approach. Nonetheless, quantification of errors and process can be time consuming. However, the development of digital assessment technology is able to meet this challenge. For example, using a digital clock drawing test, previously unappreciated behaviors are now easily quantified and can dissociate between dementia and MCI subtypes. Research presented in this paper session provides additional insight into how digital technology can be leveraged as a powerful tool to capture behavior that, until recently, was either impractical or impossible to measure.The assessment of graphomotor behavior can be challenging. In the context of a large-scale normative neuroimaging study, Colcombe and colleagues have engineered a digital Archimedes Spiral Test that includes measures of speed variability, rotational smoothness, and goodness of fit to the model. The temporal and spatial precision of these metrics is impressive. This research shows that, age predicted greater variable drawing speed, greater tracing errors, reduced rotational smoothness, and increasing drawing speed variability.MacKay-Brandt and colleagues present data using a digital version of the Trail Making Tests (TMT), one of the most commonly administered neuropsychological tests. This research provides a panel of new parameters to evaluate TMT performance, including detailed speed metrics with spatial segregation to parse circle connection time from dwell time within a circle. Interestingly, dwell time, rather than traditional total time to completion, was the strongest predictor of differences between conditions and across age. Baliga and colleagues present data on a protocol of novel cancellation tests. Memory clinic patients were classified into groups presenting with mild dementia, mild cognitive impairment, and those who were cognitively normal. Digital parameters of interest included correct responses, commissions, mean intra-response latency, and mean apple pencil touch. Using these parameters, significant between group differences were obtained. Moreover, logistic regression analyses were able to classify patients into their respective groups.It is well understood that paragraph recall tests assess a variety of underlying cognitive abilities. Andersen and colleagues studied Logical Memory recall in the Long-Life Family Study and extracted linguistic parameters that included word count, grammatical features (e.g., prepositions), and content words related to specific categories (e.g., work). Participants were classified as cognitively normal or impaired. Analyses identified distinct linguistic features of free recall that predicted cognitive status.Hershkovich and colleagues extract measured pauses and speech frequency behavior also from a paragraph recall test. A combination of paragraph recall pause duration, speech frequency parameters, and demographic variables were able to classify older adults with and without cognitive compromise. Collectively, the evidence provided in this series of papers demonstrates that digital platforms can capture and quantify highly nuanced neurocognitive behavior to enrich information available to researchers and clinicians for analysis and clinical formulations. Digital assessment technology holds promise to realize the vision of the Boston Process Approach and revolutionize neuropsychological assessment.

Reconstructing #NotMyPM from the #backdoorgovernment: a social actor analysis of Muhyiddin Yassin

In March 2020, Muhyiddin Yassin assumed power following Mahathir Mohamad’s resignation. However, his rise to office triggered significant backlash, with the hashtag “NotMyPM” trending on Twitter, reflecting widespread public discontent. This sentiment was rooted in the perception that his ascendancy contravened the people’s mandate and the democratic values upheld in Malaysia, resulting in his government being dubbed the ‘backdoor government.’ Amid the dual challenges posed by the Covid-19 pandemic and a pivotal moment for democracy, this paper contends that a strategically constructed portrayal of Muhyiddin Yassin possesses the capacity to sway public sentiment directly or indirectly, thereby reshaping negative perceptions of his government. Drawing on van Leeuwen’s socio-semantic Social Actor Representation (SAR) framework, we analyze the strategies employed by the local news sites in their portrayal of Muhyiddin Yassin when reporting his first speech as prime minister on the second Economic Stimulus Package (ESP) in March 2020. The findings unveil a consistent utilization of distinct linguistic features and strategies across various online news platforms, effectively contributing to the cultivation of Muhyiddin Yassin’s image as a responsible and trustworthy leader.

Age-related differences in associative memory recognition of Chinese characters and hippocampal subfield volumes

Associative memory is a type of hippocampal-dependent episodic memory that declines with age. Studies have examined the neural substrates underlying associative memory and considered the hippocampus holistically; however, the association between associative memory decline and volumetric change in hippocampal subfields in the context of normal aging remains uncharacterized. Leveraging the distinct linguistic features of Chinese characters to evaluate distinct types of false recognition, we investigated age-related differences in associative recognition and hippocampal subfield volumes, as well as the relationship between behavioral performance and hippocampal morphometry in 25 younger adults and 32 older adults. The results showed an age-related associative memory deficit, which was exacerbated after a 30-min delay. Older adults showed higher susceptibility to false alarm errors with recombined and orthographically related foils compared to phonologically or semantically related ones. Moreover, we detected a disproportionately age-related, time-dependent increase in orthographic errors. Older adults exhibited smaller volumes in all hippocampal subfields when compared to younger adults, with a less pronounced effect observed in the CA2/3 subfield. Group-collapsed correlational analyses revealed associations between specific hippocampal subfields and associative memory but not item memory. Additionally, multi-subfield regions had prominent associations with delayed recognition. These findings underscore the significance of multiple hippocampal subfields in various hippocampal-dependent processes including associative memory, recollection-based retrieval, and pattern separation ability. Moreover, our observations of age-related difficulty in differentiating perceptually similar foils from targets provide a unique opportunity for examining the essential contribution of individual hippocampal subfields to the pattern separation process in mnemonic recognition.

Deep Learning Based Fake News Detection on Social Media

Social media platforms become indispensable channels to discover the latest news by the Internet users. Millions of&#x0D; news is broken first, spread faster, and reach larger communities on these platforms in a very short time compared to traditional media organs. However, in contrast to traditional media, social media platforms lack of security in terms of control mechanisms to verify the reliability and accuracy of the disseminated news. This brings the need for automatic fake news detection systems for these platforms to prevent or reduce spread of false information. In this paper, we study the problem of fake news detection on social media for two languages, both of them having distinct linguistic features: Turkish and English. In this regard, we create the first real-world public dataset of Turkish fake and real news tweets, named SOSYalan, to the best of our knowledge. For English language, we carry out experiments with two benchmark datasets, BuzzFeed and ISOT. We develop deep learning based fake news detection systems for both of Turkish and English languages based on convolutional neural networks (CNN), and recurrent neural networks-long short term memory (RNN-LSTM) approaches with Word2vec embedding model. We evaluate the developed systems in terms of accuracy, precision, recall, F1-score, true-negative rate, false-positive rate, and false-negative rate metrics. The results demonstrate that the developed systems for English language produce higher accuracy rates compared to the most of the existing state-of-the-art studies. Additionally, the results confirm the superiority of our systems developed for Turkish language in comparison to very few studies conducted in this area.

Repositioning English and teaching practices in global Englishes: A critical perspective from Thai higher education

More English as a second language (ESL) and English as a foreign language (EFL) scholars are shifting attention to the Global Englishes (GE) paradigm and its attitudinal and functional impacts on English language education. Nevertheless, knowledge of this paradigm is seemingly unclear to many scholars, and there is an enormous knowledge gap on the practicality of GE implementation in Thai higher education. To minimize this gap, the current study explored the attitudes of 10 Thai EFL teachers in 10 universities across Thailand toward GE and the possibility of incorporating GE in teaching practices in Thai higher education. Qualitative data from semi-structured interviews were analysed using content analysis. The results revealed that most participants had positive perceptions toward English varieties emerging from outer and expanding circles users with diverse linguacultural backgrounds even though their Englishes have distinctive linguistic features compared to inner circle Englishes. The participants further reported their non-opposition to incorporate GE in Thai higher education, mainly if such GE integration would introduce learners to the target cultures of different English varieties, transform performance assessment, introduce new learning materials of both local and global content, expose them to various English accents, and reformulate goals of English language teaching (ELT). This implies a need for a redesign of Thai higher education ELT policies and curriculums to incorporate the GE paradigm and English varieties from the inner, outer, and expanding circles while also avoiding any biases toward native English in teaching.

Emoji, Text, and Sentiment Polarity Detection Using Natural Language Processing

Virtual users generate a gigantic volume of unbalanced sentiments over various online crowd-sourcing platforms which consist of text, emojis, or a combination of both. Its accurate analysis brings profits to various industries and their services. The state-of-art detects sentiment polarity using common sense with text only. The research work proposes an emoji-based framework for cognitive–conceptual–affective computing of sentiment polarity based on the linguistic patterns of text and emojis. The proposed emoji and text-based parser articulates sentiments with proposed linguistic features along with a combination of different emojis to generate the part of speech into n-gram patterns. In this paper, the sentiments of 650 world-famous personages consisting of 1,68,548 tweets have been downloaded from across the world. The results illustrate that the proposed natural language processing framework shows that the existence of emojis in sentiments many times seems to change the overall polarity of the sentiment. By extension, the CLDR name of the emoji is utilized to evaluate the accurate polarity of emoji patterns, and a dictionary of sentiments is adopted for evaluating the polarity of text. Eventually, the performances of three ML classifiers (SVM, DT, and Naïve Bayes) are evaluated for proposed distinctive linguistic features. The robust experiments indicate that the proposed approach outperforms the SVM classifier as compared to other ML classifiers. The proposed polarity detection generator has achieved an exceptional perspective of sentiments presented in the sentence by employing the flow of concept established, based on linguistic features, polarity inversion, coordination, and discourse patterns, surpassing the performance of extant state-of-the-art approaches.

The use of code glosses in three minute thesis presentations: A comprehensibility strategy

The Three Minute Thesis (3MT) is an academic competition that challenges graduate students to explain their research in 3 minutes to a non-specialist audience. While there have been a few studies examining rhetorical moves and interactional metadiscourse in 3MT presentations, studies focusing on features related to comprehensibility, a key competition requirement, have been lacking. This study examines how highly-specialised research is made comprehensible to non-specialist audiences through “code glosses”, communication strategies that function to facilitate understanding by reformulating, explaining, or elaborating on what has been said (Hyland, 2005). Analysing a corpus of 50 successful 3MT presentations in the electrical and computer engineering discipline, we found code glosses to be one of the most frequently used interactive metadiscourse types in the data. Significantly, Analogies and Definitions emerged as salient-enough features in our data that we added them as additional categories of code gloss, extending Hyland's (2007) two categories of Exemplifiers and Reformulators. Our analysis shows how examples, definitions, analogies, and reformulations are used in distinct ways in 3MT presentations to make the presented research accessible, coherent, and engaging to audiences. Our findings expand current understandings of code glosses, and extend the current knowledge on the 3MT genre by providing insights into its distinctive linguistic features vis-à-vis comprehensibility.

Motherese in Omani Arabic

Motherese, alternatively known as parentese, infant directed speech, or baby talk, is the spontaneous emotive manner in which mothers converse with their newborns and young children to establish mother-child communication. The occurrence of motherese in mother-child communication is attested cross-linguistically with variant extent and frequency in languages and cultures of the globe. Due to its distinct linguistic features that tease it out from adult directed speech, it is believed to have a positive role in facilitating and accelerating children’s acquisition of their mother tongue. Prominent linguistic features that characterize motherese are often the use of simplified constructions, specific lexical items intentionally meant for children, higher pitch, slower tempo, and prolonged pronunciation, among several other ones. The role these features play in enhancing children’s exposure to and comprehension of their mother tongue as well as the establishment of a compassionate mother-child relationship stand as the driving force behind its use by mothers from different walks of life cross-culturally. The Arab culture is not an exception to this tendency since motherese is attested in several varieties of Arabic. Omani Arabic as one member of these varieties does have motherese in its daily use; yet, it is not brought to light. In an attempt to sightsee such untraversed area and bring it to light, this paper is, thus, a humble endeavor to investigate the practice of using motherese among Omani mothers and children to establish mother-child communication. Results show that several features characterize Omani Arabic motherese, those that may conform to or differ from features that characterize baby talk in other languages and cultures.

Learning and teaching Chinese as a foreign language: A scoping review

AbstractDespite the growth of research in learning and teaching Chinese as a Foreign Language (CFL), no scoping review of research published in international, anglophone journals has been published so far. A total of 289 journal articles published in 95 journals were identified and used to provide a bibliometric mapping of research in CFL over three decades. Data from the sampled articles reveals a great diversity of focus in CFL research that has been conducted in more than 24 countries. The included articles also reflect an upsurge in research intensity across several key areas of focus, some of which are related to the distinctive linguistic features of the Chinese language. We also report on the research methods employed by the studies in our sample and the characteristics of their participants. Our mapping of the field identifies gaps in the existing literature which may subsequently inform any focused or comprehensive reviews. We conclude by setting out some implications for future CFL research, both in terms of substantive areas of focus and methodological approaches.

Managing Discussions in Law Blogs: from Post to Comments

The paper explores “the dialogic action game”, which takes place in law blogs through sequences of posts by law professor blogger and comments by readers, who may be either academics or an interested, non-academic public. The corpus used for this study is taken from a legal blog website: the United Kingdom Constitutional Law Association (UKCLA). The aim is to look at how law professor bloggers interact with readers in their blogs and at how participants manage the debate. By combining corpus methodology with a discourse analytic approach, the overall findings show that the law blog post encourages participants to contribute with different kinds of comments, ranging from agreement to disagreement either with the post or with other comments. The analysis has also highlighted distinctive linguistic features of posts and comments. By addressing the interests of different types of audiences who participate in discussions, law blogs testify the important role they play in the practice of law, as they build a kind of bridge between legal academics and legal practitioners.

Kolmogorov complexity metrics in assessing L2 proficiency: An information-theoretic approach.

Based on 774 argumentative writings produced by Chinese English as a foreign language (EFL) learners, this study examined the extent to which Kolmogorov complexity metrics can distinguish the proficiency levels of beginner, lower-intermediate, and upper-intermediate second language (L2) English learners. Kolmogorov complexity metric is a holistic information-theoretic approach, which measures three facets of linguistic complexity, i.e., overall, syntactic, and morphological complexity simultaneously. To assess its validity in distinguishing L2 proficiency, Kolmogorov complexity metric is compared with traditional syntactic and morphological complexity metrics as well as fine-grained syntactic complexity metrics. Results showed that Kolmogorov overall and syntactic complexity could significantly distinguish any adjacent pair of L2 levels, serving as the best separators explored in the present study. Neither Kolmogorov morphological complexity nor other complexity metrics at both the syntactic and morphological levels can distinguish between all pairs of adjacent levels. Results of correlation analysis showed that Kolmogorov syntactic complexity was not or weakly correlated with all the fine-grained syntactic complexity metrics, indicating that they may address distinct linguistic features and can complement each other to better predict different proficiency levels.